Metadata-Version: 2.2
Name: pownet
Version: 2.2.0
Summary: A simple production cost model of framework for power systems
Author-email: Phumthep Bunnak <pb585@cornell.edu>
License: MIT License
        
        
        Copyright (c) 2024 Phumthep Bunnak
        
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Project-URL: Homepage, https://github.com/Critical-Infrastructure-Systems-Lab/PowNet
Project-URL: Documentation, https://pownet.readthedocs.io/en/latest/index.html
Keywords: power_system,optimization,production_cost_model,unit_commitment
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: gurobipy>=11.0.3
Requires-Dist: highspy>=1.7.2
Requires-Dist: matplotlib>=3.7.1
Requires-Dist: networkx>=3.1.0
Requires-Dist: numpy==1.26.4
Requires-Dist: pandas>=2.1.1
Requires-Dist: pmdarima>=2.0.4
Requires-Dist: scipy>=1.11.3
Requires-Dist: scikit-learn>=1.5.2
Requires-Dist: statsmodels>=0.14.4
Requires-Dist: geopandas>=1.0.1
Requires-Dist: contextily>=1.6.2

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# PowNet: Unit Commitment / Economic Dispatch model in Python
PowNet is a least-cost optimization model for simulating the Unit Commitment and Economic Dispatch (UC/ED) of large-scale (regional to country) power systems. In PowNet, a power system is represented by a set of nodes that include power plants, high-voltage substations, and import/export stations (for cross-border systems). The model schedules and dispatches the electricity supply from power plant units to meet hourly electricity demand in substations at a minimum cost. It considers the techno-economic constraints of both generating units and high-voltage transmission network. The power flow calculation is based on a Direct Current (DC) network (with N-1 criterion), which provides a reasonable balance between modelling accuracy and data and computational requirements. PowNet can easily integrate information about variable renewable resources (e.g., hydro, wind, solar) into the UC/ED process. For example, it can be linked with models that estimate the electricity supply available from renewable resources as a function of the climatic conditions. In addition, PowNet has provision to account for the effect of droughts on the generation of dispatchable thermal units (e.g., coal, oil, gas-fired units) that depend on freshwater availability. These features facilitate the application of PowNet to problems in the water-energy nexus domain that investigate the impact of water availability on electricity supply and demand. 

Read the PowNet Documentation here: https://pownet.readthedocs.io/en/latest


## Installation

Download or clone the PowNet 2.0 repository to your local machine. For example: if we want to clone to "C://user/pownet",

```
git clone https://github.com/your-username/pownet.git C://user/pownet
```

Next, open a terminal and navigate to the directory where you cloned the repository:

```
cd C://user/pownet
```

The following step is highly recommended: creating a virtual environment to manage dependencies. If using Conda, you can create an envrionment named "pownet":

```
conda create --name pownet
conda activate pownet
```

Now, you can install this PowNet package using pip, which is a manager for Python packages:

```
pip install -e .
```

This command installs the package in "editable" mode (-e) using pyproject.toml that is located in the root directory of PowNet. The editable mode allows you to edit PowNet codebase when you need to modify or implement new features. The pyproject.toml file specifies the dependencies required to run PowNet.

A quick start tutorial to run a simple case study is provided here: https://pownet.readthedocs.io/en/latest/pages/quickstarter.html

## Overview of PowNet

![overview_pownet](./docs/overview_pownet.png)


